def __init__( self, featurestore_id, featurestore_name, created, hdfs_store_path, project_name, project_id, featurestore_description, inode_id, offline_featurestore_name, hive_endpoint, online_enabled, num_feature_groups=None, num_training_datasets=None, num_storage_connectors=None, online_featurestore_name=None, mysql_server_endpoint=None, online_featurestore_size=None, ): self._id = featurestore_id self._name = featurestore_name self._created = created self._hdfs_store_path = hdfs_store_path self._project_name = project_name self._project_id = project_id self._description = featurestore_description self._inode_id = inode_id self._online_feature_store_name = online_featurestore_name self._online_feature_store_size = online_featurestore_size self._offline_feature_store_name = offline_featurestore_name self._hive_endpoint = hive_endpoint self._mysql_server_endpoint = mysql_server_endpoint self._online_enabled = online_enabled self._num_feature_groups = num_feature_groups self._num_training_datasets = num_training_datasets self._num_storage_connectors = num_storage_connectors self._feature_group_api = feature_group_api.FeatureGroupApi(self._id) self._storage_connector_api = storage_connector_api.StorageConnectorApi( self._id) self._training_dataset_api = training_dataset_api.TrainingDatasetApi( self._id) self._expectations_api = expectations_api.ExpectationsApi(self._id) self._feature_group_engine = feature_group_engine.FeatureGroupEngine( self._id) self._transformation_function_engine = ( transformation_function_engine.TransformationFunctionEngine( self._id)) self._feature_view_engine = feature_view_engine.FeatureViewEngine( self._id)
def __init__( self, featurestore_id, featurestore_name, created, hdfs_store_path, project_name, project_id, featurestore_description, inode_id, offline_featurestore_name, hive_endpoint, online_enabled, online_featurestore_name=None, mysql_server_endpoint=None, online_featurestore_size=None, ): self._id = featurestore_id self._name = featurestore_name self._created = created self._hdfs_store_path = hdfs_store_path self._project_name = project_name self._project_id = project_id self._description = featurestore_description self._inode_id = inode_id self._online_feature_store_name = online_featurestore_name self._online_feature_store_size = online_featurestore_size self._offline_feature_store_name = offline_featurestore_name self._hive_endpoint = hive_endpoint self._mysql_server_endpoint = mysql_server_endpoint self._online_enabled = online_enabled self._feature_group_api = feature_group_api.FeatureGroupApi(self._id) self._storage_connector_api = storage_connector_api.StorageConnectorApi( self._id) self._training_dataset_api = training_dataset_api.TrainingDatasetApi( self._id) self._feature_group_engine = feature_group_engine.FeatureGroupEngine( self._id)
def __init__( self, name, version, featurestore_id, description="", partition_key=None, primary_key=None, hudi_precombine_key=None, featurestore_name=None, created=None, creator=None, id=None, features=None, location=None, online_enabled=False, time_travel_format=None, statistics_config=None, validation_type="NONE", expectations=None, ): super().__init__(featurestore_id) self._feature_store_id = featurestore_id self._feature_store_name = featurestore_name self._description = description self._created = created self._creator = creator self._version = version self._name = name self._id = id self._features = [ feature.Feature.from_response_json(feat) if isinstance(feat, dict) else feat for feat in features ] self._location = location self._online_enabled = online_enabled self._time_travel_format = (time_travel_format.upper() if time_travel_format is not None else None) if id is not None: # initialized by backend self._primary_key = [ feat.name for feat in self._features if feat.primary is True ] self._partition_key = [ feat.name for feat in self._features if feat.partition is True ] if time_travel_format is not None and time_travel_format.upper( ) == "HUDI": # hudi precombine key is always a single feature self._hudi_precombine_key = [ feat.name for feat in self._features if feat.hudi_precombine_key is True ][0] else: self._hudi_precombine_key = None self.statistics_config = statistics_config else: # initialized by user self.primary_key = primary_key self.partition_key = partition_key self._hudi_precombine_key = ( hudi_precombine_key.lower() if hudi_precombine_key is not None and time_travel_format is not None and time_travel_format.upper() == "HUDI" else None) self.statistics_config = statistics_config self._data_validation_engine = data_validation_engine.DataValidationEngine( featurestore_id, self.ENTITY_TYPE) self._validation_type = validation_type.upper() if expectations is not None: self._expectations_names = [ expectation.name for expectation in expectations ] else: self._expectations_names = [] self._feature_group_engine = feature_group_engine.FeatureGroupEngine( featurestore_id)
def __init__( self, name, version, description, featurestore_id, partition_key=None, primary_key=None, featurestore_name=None, created=None, creator=None, descriptive_statistics=None, feature_correlation_matrix=None, features_histogram=None, cluster_analysis=None, id=None, features=None, location=None, jobs=None, desc_stats_enabled=None, feat_corr_enabled=None, feat_hist_enabled=None, cluster_analysis_enabled=None, statistic_columns=None, num_bins=None, num_clusters=None, corr_method=None, online_enabled=False, hudi_enabled=False, default_storage="offline", ): self._feature_store_id = featurestore_id self._feature_store_name = featurestore_name self._description = description self._created = created self._creator = creator self._version = version self._descriptive_statistics = descriptive_statistics self._feature_correlation_matrix = feature_correlation_matrix self._features_histogram = features_histogram self._cluster_analysis = cluster_analysis self._name = name self._id = id self._features = [ feature.Feature.from_response_json(feat) for feat in features ] self._location = location self._jobs = jobs self._desc_stats_enabled = desc_stats_enabled self._feat_corr_enabled = feat_corr_enabled self._feat_hist_enabled = feat_hist_enabled self._cluster_analysis_enabled = cluster_analysis_enabled self._statistic_columns = statistic_columns self._num_bins = num_bins self._num_clusters = num_clusters self._corr_method = corr_method self._online_enabled = online_enabled self._default_storage = default_storage self._hudi_enabled = hudi_enabled self._primary_key = primary_key self._partition_key = partition_key self._feature_group_engine = feature_group_engine.FeatureGroupEngine( featurestore_id)
def __init__( self, name, version, featurestore_id, description="", partition_key=None, primary_key=None, hudi_precombine_key=None, featurestore_name=None, created=None, creator=None, id=None, features=None, location=None, jobs=None, desc_stats_enabled=None, feat_corr_enabled=None, feat_hist_enabled=None, statistic_columns=None, online_enabled=False, time_travel_format=None, statistics_config=None, ): super().__init__(featurestore_id) self._feature_store_id = featurestore_id self._feature_store_name = featurestore_name self._description = description self._created = created self._creator = creator self._version = version self._name = name self._id = id self._features = [ feature.Feature.from_response_json(feat) if isinstance(feat, dict) else feat for feat in features ] self._location = location self._jobs = jobs self._online_enabled = online_enabled self._time_travel_format = (time_travel_format.upper() if time_travel_format is not None else None) if id is not None: # initialized by backend self.statistics_config = StatisticsConfig( desc_stats_enabled, feat_corr_enabled, feat_hist_enabled, statistic_columns, ) self._primary_key = [ feat.name for feat in self._features if feat.primary is True ] self._partition_key = [ feat.name for feat in self._features if feat.partition is True ] if time_travel_format is not None and time_travel_format.upper( ) == "HUDI": # hudi precombine key is always a single feature self._hudi_precombine_key = [ feat.name for feat in self._features if feat.hudi_precombine_key is True ][0] else: self._hudi_precombine_key = None else: # initialized by user self.statistics_config = statistics_config self._primary_key = primary_key self._partition_key = partition_key self._hudi_precombine_key = ( hudi_precombine_key if time_travel_format is not None and time_travel_format.upper() == "HUDI" else None) self._feature_group_engine = feature_group_engine.FeatureGroupEngine( featurestore_id)
def __init__( self, name, version, description, featurestore_id, partition_key=None, primary_key=None, featurestore_name=None, created=None, creator=None, id=None, features=None, location=None, jobs=None, desc_stats_enabled=None, feat_corr_enabled=None, feat_hist_enabled=None, statistic_columns=None, online_enabled=False, hudi_enabled=False, default_storage="offline", statistics_config=None, ): self._feature_store_id = featurestore_id self._feature_store_name = featurestore_name self._description = description self._created = created self._creator = creator self._version = version self._name = name self._id = id self._features = [ feature.Feature.from_response_json(feat) for feat in features ] self._location = location self._jobs = jobs self._online_enabled = online_enabled self._default_storage = default_storage self._hudi_enabled = hudi_enabled if id is None: # Initialized from the API self._primary_key = primary_key self._partition_key = partition_key else: # Initialized from the backend self._primary_key = [f.name for f in self._features if f.primary] self._partition_key = [ f.name for f in self._features if f.partition ] if id is not None: # initialized by backend self.statistics_config = StatisticsConfig( desc_stats_enabled, feat_corr_enabled, feat_hist_enabled, statistic_columns, ) self._primary_key = [ feat.name for feat in self._features if feat.primary is True ] self._partition_key = [ feat.name for feat in self._features if feat.partition is True ] else: # initialized by user self.statistics_config = statistics_config self._primary_key = primary_key self._partition_key = partition_key self._feature_group_engine = feature_group_engine.FeatureGroupEngine( featurestore_id) self._statistics_engine = statistics_engine.StatisticsEngine( featurestore_id, self.ENTITY_TYPE)